Speed optimization for AI on mobile devices and a cleanup of TensorFlow’s cluttered APIs is more than cosmetic — these changes will shape how developers and businesses train AI systems. But the news that caught my eye was the release of TensorFlow for federated learning.

TensorFlow Federated will provide distributed machine learning for developers to train models across many mobile devices without data ever leaving those devices. Encryption provides an additional layer of privacy, and weights from models trained on mobile devices are shared with a central model for continuous learning.

The Google AI research team debuted federated learning as a way to train AI on-device compute power in April 2017 and since then has used it for personalization in GBoard and search for Android smartphones.

TensorFlow Privacy, a library of deep learning models with some privacy guarantees, also made its debut this week.

Confidence in privacy could lead to more sharing of data beyond publicly available datasets, and perhaps even more sharing of data between organizations.

This brings to mind a number of industry verticals — first and foremost, health care.

Federated learning has been important for Owkin, a company backed by GV (formerly Google Ventures) that closed a funding round of an undisclosed amount earlier this week. The company created a platform based on machine learning that’s used by hospitals, academic centers, and pharmaceutical and biotech companies to do things like predict disease evolution and drug toxicity.

Federated learning could also change the way programmers making AI collect data without causing an uproar over user privacy.

When more people are empowered to control their personal data, researchers could approach them and ask them to volunteer their data to help train models.

The virtues of federated data were recently summarized nicely by University of Michigan professor Mi Zhang, who argues that federated learning doesn’t just have privacy benefits, but could harness the increasing power of mobile devices to disrupt cloud computing.

“As compute resources inside end devices such as mobile phones are becoming increasingly powerful, especially with the emergence of AI chipsets, AI is moving from clouds and datacenters to end devices. Federated learning provides a privacy-preserving mechanism to effectively leverage those decentralized compute resources inside end devices to train machine learning models,” Zhang recently told Synced.

Google researchers laid out how to scale federated learning and some of the difficulties they’ve encountered thus far in a paper last month. Challenges include an inability to inspect training examples, bandwidth issues, and even potential bias due in part to the fact that federated learning only trains AI models when a mobile device is charging and using a Wi-Fi connection.

There may be barriers to the widespread adoption of federated learning, but look for companies like Facebook, maker of the PyTorch machine learning framework, to adopt similar techniques. It could become even more valuable to Facebook in the future, in light of CEO Mark Zuckerberg’s comments this week about privacy’s role in the future.

Even if competitors aren’t quick to admit it, the actions Google takes with its open source framework — which has now seen more than 41 million pip installs since it was created — can shape the rest of the ecosystem.

By flying unmanned aerial vehicles equipped with infrared imaging, a team from the Queensland University of Technology (QUT) can spot marsupials even under the cover of the eucalyptus trees where they live. (via Geek)